Robot and control method thereof

ABSTRACT

Disclosed herein are a feature point used to localize an image-based robot and build a map of the robot and a method of extracting and matching an image patch of a three-dimensional (3D) image, which is used as the feature point. It is possible to extract the image patch converted into the reference image using the position information of the robot and the 3D position information of the feature point. Also, it is possible to obtain the 3D surface information with the brightness values of the image patches to obtain the match value with the minimum error by a 3D surface matching method of matching the 3D surface information of the image patches converted into the reference image through the ICP algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Korean Patent Application No.2009-0000891, filed on Jan. 6, 2009 in the Korean Intellectual PropertyOffice, the disclosure of which is incorporated herein by reference.

BACKGROUND

1. Field

The present invention relates to a feature point used to localize animage-based robot and build a map of the robot, and, more particularly,to a method of extracting and matching an image patch of athree-dimensional (3D) image, which is used as the feature point.

2. Description of the Related Art

Generally, a robot is a mechanism to perform a motion resembling anaction of a human being using an electrical or magnetic operation.Recent robots have been utilized in various applications due to thedevelopment of sensors and controllers. For example, a robot can be usedfor housework, public service, or a conveying robot or worker-assistingrobot on a production line. For the robot to move under its ownself-control, it is necessary to simultaneously perform a localizationprocess to recognize its position without preliminary information on aperipheral environment and a map building process to build a map fromthe information on the environment. This overall process is calledsimultaneous localization and map-building (SLAM).

The SLAM is a technology to acquire an image through an all-directionview camera, build a map in real time, and recognize a position. TheSLAM is used as an appropriate substitute for indoor localization.However, the SLAM finds a feature point from the acquired image andextracts an image patch having several pixels around the feature point,i.e., extracts an image patch of a 3D image with a predetermined size(15×15 pixels) only from the image itself. For this reason, when aspatial relationship between the camera and the feature point (3Dposition) changes due to the movement of the camera, the size anddirection of the feature point in the image change, with the result thatseveral errors are included in the position of the robot. Thus, it isnot possible to accurately localize the robot and build a map of therobot.

SUMMARY

Therefore, it is an aspect of the invention to provide a method ofextracting and matching an image patch of an image, using positioninformation of an image-based robot and 3D position information of afeature point, to localize the robot and build a map of the robot.

Additional aspects and/or advantages will be set forth in part in thedescription which follows and, in part, will be apparent from thedescription, or may be learned by practice of the invention.

The foregoing and/or other aspects of the present invention are achievedby providing a control method of a robot, including acquiring a 3D imageof an environment where the robot moves, extracting a feature point fromthe acquired 3D image and converting a plane including the extractedfeature point into a reference image, and extracting image patches fromthe reference image and matching the image patches.

The converting the plane including the feature point into the referenceimage may include converting the plane including the feature point intoa reference distance to acquire an image corresponding to the referencedistance.

The extracting the image patches from the reference image may includeobtaining a conversion matrix with the reference image to extractpredetermined pixel-unit image patches from the reference distance.

The matching the image patches may include obtaining 3D surfaceinformation using brightness values of the image patches and matchingthe 3D surface information three-dimensionally using an iterativeclosest point (ICP) algorithm.

The control method may further include determining whether thebrightness values of the 3D surface information satisfies apredetermined reference value, and, when it is determined that thebrightness values of the 3D surface information satisfies thepredetermined reference value, the 3D surface information may be matchedby the ICP algorithm.

The foregoing and/or other aspects of the present invention may also beachieved by providing a control method of a robot, including acquiring a3D image of an environment where the robot moves, extracting a featurepoint from the acquired 3D image and converting the extracted featurepoint into a reference distance, extracting an image patch of an imagecorresponding to the reference distance, obtaining 3D surfaceinformation using a brightness value of the image patch, and matchingthe 3D surface information three-dimensionally using an ICP algorithm.

The extracting the image patch of the image corresponding to thereference distance may include obtaining a conversion matrix with theimage corresponding to the reference distance and extractingpredetermined pixel-unit image patches from the reference distance.

The foregoing and/or other aspects of the present invention may also beachieved by providing a robot including an image acquisition unit toacquire a 3D image of an environment where the robot moves and a controlunit to extract a feature point from the acquired 3D image and convert aplane including the feature point into a reference image and to extractimage patches from the reference image and match the image patches.

The control unit may convert the plane including the feature point intoa reference distance, acquire an image corresponding to the referencedistance, and convert the acquired image into the reference image.

The control unit may obtain a conversion matrix with the reference imageto extract predetermined pixel-unit image patches from the referencedistance.

The control unit may obtain 3D surface information using brightnessvalues of the image patches, and match the 3D surface informationthree-dimensionally using an ICP algorithm.

The control unit may determine whether the brightness values of the 3Dsurface information satisfies a predetermined reference value, and, whenit is determined that the brightness values of the 3D surfaceinformation satisfies the predetermined reference value, match the 3Dsurface information using the ICP algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects and advantages of the invention will becomeapparent and more readily appreciated from the following description ofthe embodiments, taken in conjunction with the accompanying drawings, ofwhich:

FIG. 1 is a perspective view illustrating the external appearance of arobot according to an embodiment of the present invention;

FIG. 2 is a control block diagram of the robot according to theembodiment of the present invention;

FIG. 3 is a flow chart illustrating a method of extracting and matchingan image patch to localize a robot and build a map of the robotaccording to an embodiment of the present invention;

FIG. 4 is a perspective view illustrating an example of extracting afeature point from a reference image to localize a robot and build a mapof the robot according to an embodiment of the present invention; and

FIG. 5 is a view illustrating an example of an ICP algorithm to matchtwo surfaces to build a map of a robot according to an embodiment of thepresent invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the embodiment of the presentinvention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout. The embodiment is described below to explain the presentinvention by referring to the figures.

FIG. 1 is a perspective view illustrating the external appearance of arobot 10 according to an embodiment of the present invention.

In FIG. 1, the robot 10 is a two-leg walking robot that moves in anupright posture using two legs 11 in the same manner as a human being.The robot 10 includes a body 12, two arms 13 mounted to opposite lateralsides of the body 12, and a head 14 mounted to the top of the body 12.Feet 15 and hands 16 are mounted to the legs 11 and the arms 13,respectively. The robot 10 moves self-controllably in an unknownenvironment.

FIG. 2 is a control block diagram of the robot according to theembodiment of the present invention. The robot includes an imageacquisition unit 20, an image processing unit 22, a control unit 24, astorage unit 26, and a drive unit 28.

The image acquisition unit 20 may be implemented by a 3D measurementdevice (for example, a stereo camera or a time-of-flight camera) whichphotographs an image on a movement path of the robot 10, when the robot10 moves in an unknown environment, to acquire 3D information of variousobjects (for example, static objects or dynamic objects such as walls orobstacles) located on the path in real time. The 3D measurement devicemeasures distance information of an object detected by a sensor andpixels of the camera as well as image information on each pixel of thecamera, which may be utilized as much more information in SLAM or anobstacle detection method.

When the 3D information of the image acquired by the image acquisitionunit 20 is inputted, the image processing unit 22 extracts a featurepoint of the movement path of the robot 10. The feature point is aquantitative point which is not changed depending upon time and aviewing angle when the point is selected in an environment where therobot 10 moves. In a normal image, a point which is not changed evenwhen viewed at any angle is found using a corner image or an imagepatch. In this embodiment, a plane including a feature point isconverted into a reference image, using the image acquisition unit 20,which is the 3D measurement device, to extract the feature point.

When feature points extracted by the image processing unit 22 areinputted, the control unit 24 obtains 3D positions of the respectivefeature points and converts the respective features into a referencedistance to extract image patches corresponding to the referencedistance, i.e., image patches converted into the reference image.

Also, the control unit 24 obtains 3D surface information with brightnessvalues of the extracted image patches, performs an ICP algorithm tomatch the image patches converted into the reference image, and obtainsa match value with the minimum error to build map information. Thecontrol unit may be a central processing unit (CPU).

The storage unit 26 is a memory to store data of the feature pointsextracted by the image processing unit 22, the image patches convertedinto the reference image by the control unit 24, and the map informationbuilt by the control unit 24. It is necessary for the storage unit 26 tostore the current position and the final target position of the robot10.

The drive unit 28 drives the robot 10, such that the robot 10 movesself-controllably along the path without the collision of walls orobstacles, based on the map information built by the control unit 24.

Hereinafter, a control method of the robot with the above-statedconstruction will be described.

FIG. 3 is a flow chart illustrating a method of extracting and matchingan image patch to localize a robot and build a map of the robotaccording to an embodiment of the present invention.

In FIG. 3, when the robot 10 starts to move in an unknown environment,an image on a movement path of the robot 10 is photographed by the imageacquisition unit 20 to acquire an all-direction image including 3Dinformation of various objects (for example, static objects or dynamicobjects) located on the path in a mixed state (100).

Subsequently, the image processing unit 22 receives the 3D informationof the all-direction image acquired by the image acquisition unit 20 andextracts feature points P of the movement path of the robot 10 (102). Itis necessary for each feature point to exhibit a quantitative propertyin which the feature point is not changed depending upon time and aviewing angle when the point is selected in an environment where therobot 10 moves.

Subsequently, the control unit 18 receives the feature points Pextracted by the image processing unit 22 and obtains 3D (X, Y, Z)coordinates of the feature points P and a plane including the featurepoints P through stereo image processing (104). Subsequently, as shownin FIG. 4, the control unit 18 converts the plane including therespective feature points P into a reference distance (106), andextracts and registers image patches corresponding to the referencedistance, i.e., image patches of the reference image extracted in unitof a pixel having a predetermined size (108).

FIG. 4 is a prespective view illustrating an example of extracting afeature point from a reference image to localize a robot and build a mapof the robot according to an embodiment of the present invention.

In FIG. 4, a perpendicular vector of an image patch of a plane includinga feature point P at a reference distance is always directed to theimage acquisition unit 20. It is possible to obtain an image conversionmatrix T using an image corresponding to the reference distance and theposition and direction of an actual image. Since an image patch from aregular distance is extracted by extracting an image patch correspondingto the reference distance using the image conversion matrix, it ispossible to eliminate an error occurring from a 3D distance between theimage acquisition unit 20 and the feature point P.

Referring back to FIG. 3, the control unit 24 obtains 3D surfaceinformation using the brightness values of the pixel-unit image patchesconverted into the reference image (110), and performs an ICP algorithmto match the image patches converted into the reference image, therebymatching the 3D surface information of the image patches converted intothe reference image (112). The ICP algorithm is a general method ofregistering two surfaces in a repeated manner such that brightnessvalues of the two surfaces becomes minimum. A method of performing theICP algorithm is disclosed in several documents, such as Korean PatentApplication Publication No. 2003-0040701 and No. 2004-0094230.

More specifically, the ICP algorithm used to match the 3D surfaceinformation is a general method of registering two surfaces. The ICPalgorithm obtains a relationship between rotation and translation ofrigid body motion in which the total distance sum of points existing onthe two surfaces becomes minimum. There are several methods of embodyingthe ICP algorithm.

An ICP algorithm suggested by Horn solves the registration problembetween two different coordinate systems using unit quaternions by acovariance matrix. [B. K. P. Horn, ‘Closed-Form Solution of AbsoluteOrientation Using Unit Quaternions’, Journal of the Optical Society ofAmerica, A, Vol. 4. pp. 629-642, April 1987]. On the other hand, an ICPalgorithm suggested by Besl and McKay, which is a method widely used inconnection with the registration problem, repeatedly finds pairs of thenearest points between two data sets, without the extraction of a pointof agreement, to optimize the registration. [P. J. Besl and N. D. McKay,‘A method for Registration of 3-D shapes’, IEEE Transactions on PatternAnalysis and Machine Intelligence, Vol. 14, no. 2, pp. 239-256, February1992].

When matching the surface information of the 3D images using the ICPalgorithm, there occurs an error if a predetermined reference value (abrightness value having a predetermined magnitude) is not satisfied. Inthis embodiment, therefore, 3D surface information satisfying thepredetermined reference values are matched, whereby it is thought thatthis embodiment strongly deals with an error in localizing the robot 10and building a map of the robot 10 in a general environment (an unknownenvironment).

The surface information of the 3D image is matched as shown in FIG. 5through the performance of the ICP algorithm. When the value of thebrightness exceeds the predetermined magnitude, during the surfacematching of the ICP algorithm, an error occurs, with the result thatdistorted map information may be included. Consequently, the controlunit 24 calculates the brightness values of the 3D surface informationmatched by the ICP algorithm and determines whether the brightnessvalues of the 3D surface information satisfies the predeterminedreference value (114).

When it is determined at operation 114 that the brightness values of the3D surface information satisfies the predetermined reference value, thecontrol unit 24 matches the 3D surface information of which thebrightness values satisfy the predetermined reference value and buildsmap information with the matched values at which the error is minimum,thereby performing the localization of the robot 10 with an improvedperformance of the SLAM (116).

In this embodiment, meanwhile, a mobile robot that movesself-controllably, such as a housework helping robot, a public servicerobot, or an intelligent humanoid robot, was described as an example ofthe robot 10, to which, however, the embodiments of the presentinvention are not limited. The embodiments of the present invention areapplicable to various applications. For example, the robot may be loadedin a mobile phone or in a wearable form to recognize the currentposition and inform of an advancing direction. Alternatively, the robotmay be loaded in a vehicle to guide the vehicle to reach the destinationin an unmanned fashion or automatically park the vehicle.

As is apparent from the above description, the embodiments of thepresent invention are capable of extracting the image patch convertedinto the reference image using the position information of the robot andthe 3D position information of the feature point, thereby eliminating anerror occurring from the 3D distance between the camera and the featurepoint. Also, the embodiments of the present invention are capable ofobtaining the 3D surface information with the brightness values of theimage patches to obtain the match value with the minimum error by a 3Dsurface matching method of matching the 3D surface information of theimage patches converted into the reference image through the ICPalgorithm, thereby accurately localizing the robot and building the mapof the robot.

Although a few embodiments of the present invention have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from theprinciples and spirit of the invention, the scope of which is defined inthe claims and their equivalents.

What is claimed is:
 1. A control method of a robot, comprising:acquiring a 3D image of an environment where the robot moves; extractinga feature point from the acquired 3D image and converting a planeincluding the extracted feature point into a reference image; andextracting image patches from the reference image using a centralprocessing unit (CPU) and matching the image patches by obtaining 3Dsurface information using brightness values of the image patches usingthe CPU, wherein the reference image is an image corresponding to areference distance.
 2. The control method according to claim 1, whereinthe converting the plane including the feature point into the referenceimage comprises converting the plane including the feature point intothe reference distance to acquire the image corresponding to thereference distance.
 3. The control method according to claim 2, whereinthe extracting the image patches from the reference image comprisesobtaining a conversion matrix with the reference image to extractpredetermined pixel-unit image patches from the reference distance. 4.The control method according to claim 3, wherein the matching the imagepatches further comprises: matching the 3D surface informationthree-dimensionally using an lCPiterative closest point (“ICP”)algorithm.
 5. The control method according to claim 4, furthercomprising determining whether brightness values of the 3D surfaceinformation satisfies a predetermined reference value, wherein, when itis determined that the brightness values of the 3D surface informationsatisfies the predetermined reference value, the 3D surface informationis matched by the ICP algorithm.
 6. A control method of a robot,comprising: acquiring a 3D image of an environment where the robotmoves; extracting a feature point from the acquired 3D image andconverting an image having the extracted feature point into an imagecorresponding to the reference distance; extracting an image patch fromthe image corresponding to the reference distance using a centralprocessing unit (CPU); obtaining 3D surface information using abrightness value of the image patch; and matching the 3D surfaceinformation three-dimensionally using an iterative closest point (“ICP”)algorithm executed by the CPU.
 7. The control method according to claim6, wherein the extracting the image patch of the image corresponding tothe reference distance comprises obtaining a conversion matrix with theimage corresponding to the reference distance and extractingpredetermined pixel-unit image patches from the reference distance.
 8. Arobot comprising: an image acquisition unit to acquire a 3D image of anenvironment where the robot moves; and a control unit to extract afeature point from the acquired 3D image, to convert a plane includingthe feature point into a reference image, to extract image patches fromthe reference image by obtaining 3D surface information using brightnessvalues of the image patches, and to match the image patches, wherein thereference image is an image corresponding to a reference distance. 9.The robot according to claim 8, wherein the control unit converts theplane including the feature point into the reference distance, acquiresthe image corresponding to the reference distance, and converts theacquired image into the reference image.
 10. The robot according toclaim 9, wherein the control unit obtains a conversion matrix with thereference image to extract predetermined pixel-unit image patches fromthe reference distance.
 11. The robot according to claim 10, wherein thecontrol unit matches the 3D surface information three-dimensionallyusing an iterative closest point (“ICP”) algorithm.
 12. The robotaccording to claim 11, wherein the control unit determines whether thebrightness values of the 3D surface information satisfies apredetermined reference value, and, when it is determined that thebrightness values of the 3D surface information satisfies thepredetermined reference value, matches the 3D surface information usingthe ICP algorithm.
 13. A robot comprising: an image acquisition unit toacquire a 3D image of an environment where the robot moves; a controlunit to extract a feature point from the acquired 3D image, to convertan image having the extracted feature point into an image correspondingto the reference distance, to extract an image patch from the imagecorresponding to the reference distance, to obtain 3D surfaceinformation using a brightness value of the image patch, and to matchthe 3D surface information three-dimensionally using an iterativeclosest point (“ICP”) algorithm.
 14. The robot according to claim 13,wherein the control unit extracts the image patch of the imagecorresponding to the reference distance by obtaining a conversion matrixwith the image corresponding to the reference distance and extractingpredetermined pixel-unit image patches from the reference distance.